Thoughtfully designed and executed pricing research enables companies to determine optimal price points that allow new products and innovations to fully deliver value to customers. Quantifying customer willingness-to-pay empowers organizations to confidently launch products that customers want while earning healthy margins to fund growth.
In this article, I'll discuss three popular survey-based research techniques organizations can use to conduct pricing research:
Each technique has its pros and cons, but we can all agree that using data to inform decision making will help brands build better products and services.
Van Westendorp
This pricing research technique, also known as the Price Sensitivity Meter, is an age-old approach to finding an optimal price point. It's simple and involves four questions that ask respondents the following:
At what price is this product too expensive?
At what price is this product getting expensive but still considerable?
At what price is this product a great value?
At what price is this product too cheap?
Plotting responses to these questions shows an acceptable range of prices and an optimal price point.
While this method is simple for survey participants, there are flaws that a researcher must be aware of. The first is that a respondent typically only evaluates one product, without competitive context. This could result in significantly overstating the optimal price (i.e., suggesting a price that is much higher than competitor products that exist today) or potentially under-stating it if the description of the product doesn't align with competitors in the category.
In addition, the product is typically locked, meaning the research will not be able to provide recommendations for optimizing features to drive a higher price or recommend which features to swap out to increase profitability.
But the limitation to be most cautious of is that the Van Westendorp questions do not directly ask if respondents would actually purchase the product at specific price points. Certainly I can tell you a price that I think is expensive and/or good value - but that doesn't mean I would purchase it at that price point.
The good news is that there is an extension called the Newton-Miller Smith model that combines Van Westendorp data with an additional purchase intent question. This extension creates a purchase probability curve, or price sensitivity curve, across price ranges.
For example, a respondent may indicate up to $50 is entirely acceptable yet they only have a 30% likelihood of actually buying at that price. The intent likelihood clarifies the curve and expected conversion at each price level. This enhanced granularity aids significantly in zeroing in on revenue-maximizing price points - a much more powerful output than just Van Westendorp data alone.
In summary, there are risks that the results of a Van West can be completely outside your expectations. If you do choose to use Van West, we strongly recommend adding the Newton Miller Smith extension to layer on the pivotal purchase intent probabilities that allow honing pricing decisions for optimal lift. Our preferred use of Van Westendorp is early on in the product development lifecycle when you're looking for directional insights on the price ranges and expectations for products that are new to world and have little competition today.
Gabor-Granger
Also known as Price Laddering, this technique starts with an initial price point for a product or service. If the respondent agrees to purchase the product at that price point, a higher price is presented. If they decline, a lower price is tested. This continues until willingness to pay (WTP) thresholds are identified.
For example, when testing freelancing services priced at $100/hour, the researcher would ask about $150 if accepted or $75 if rejected. Eventually, a peak willingness price emerges, say $150.
This fast and straightforward method determines the price sensitivity curve for the products and services tested. But, while easy to implement, Gabor-Granger has limitations:
It only evaluates pricing for one product or service at a time, preventing bundling pricing optimization with feature optimization.
It typically does not incorporate competitive pricing data and risks developing unrealistically high/low pricing.
Requires separate price laddering studies for every distinct offering and fails to leverage product commonalities.
We believe respondents can really only answer ~3 of these questions before they "catch on to the game", which could result in sparse data if many price points are tested or find someone's local WTP and not their max WTP.
Gabor-Granger offers directional input on pricing thresholds yet falls short of the holistic optimization possible through conjoint analysis and simulations factoring in product configurations, features, competitive data, and scenario modeling. It remains a fast, low-effort option, but strategic applicability is restricted. For most advanced pricing research needs, conjoint leads to far richer insights and revenue lift potential.
Conjoint Analysis
Conjoint analysis provides immense strategic value by quantifying customer preferences for product attributes and specific levels of those attributes. This technique tests combinations to quantify preferences and price sensitivity, estimates the price elasticity of demand, and identifies revenue-maximizing prices. This technique best mimics real purchase tradeoffs and enables holistic product/price optimization inclusive of competition. To us, conjoint is the most insightful (and intensive) survey-based approach, and researchers can use this understanding to model a wide range of feature and pricing configurations.
For example, we could evaluate high, medium, and low specifications for processor speed and storage capacity coupled with premium, moderate, and low pricing alternatives if we were tasked with helping bring a new tablet to market. Testing these variables in combination expands scenario modeling.
The output of conjoint analysis is a model that estimates a consumers likelihood to purchase a particular tablet configuration over another. The team can use this output to determine the optimal offering that will balance adoption rates and profit goals. This enables incredibly precise price elasticity measurement unavailable through other means.
Better yet, you can inject the competitive context to benchmark optimal configurations against current or potential offerings in the marketplace, ensuring your pricing remains attuned to real-world consumer decision drivers.
Conjoint analysis empowers recommendations like:
"Priced at $299 with high processor speed but moderate storage, 22% of prospective customers would purchase our product over competitive options, generating $X million revenue at Y% margins."
Such predictive and financially quantified guidance boosts confidence for executives when deciding pricing structures. While there are many assumptions that underly the conjoint model, and exact predictions of revenue and margins are difficult, the output is still extremely useful in the relative context of comparing one alternative to another. This scenario modeling, possible only through conjoint analysis, is unmatched in capturing the interplay between product attributes, pricing, positioning, and customer preferences to identify revenue-maximizing sweet spots.
Of course, there are drawbacks to conjoint analysis, including complexity, cost and timing. Designing a conjoint study requires careful consideration of the product or service being tested, the market landscape and the target audience. From a timing perspective, if you are in the early stages product concepting, you may have too many ideas to test in a conjoint. Earlier phases of research that narrow the scope or refine what you're planning to bring to market could prove useful.
Interpreting the results can also be complex. Partnering with a strategic consultant who has a deep understanding of the methodology AND your business will be crucial.
And while it may cost more and take longer than a Van West or Gabor-Granger, the simulator tool is an evergreen deliverable that can be used for months after the research is completed. This would suggest that while the initial buy-in may be high, there will be little need to re-run the research if product specs or the roadmap changes.
How Pricing Research Empowers Organizations
Pricing techniques range from quick directional inputs to deeply strategic recommendations. Each methodology serves selected needs, but conjoint analysis excels at holistic optimization, factoring in configurations, features, positioning, competitive offerings, and scenario projections.
By quantifying buyer preferences and simulating scenarios to pinpoint ideal pricing structures, organizations can gain insights to prosper.